The present invention relates to the field of signal processing, and more particularly, to a method for detecting weak signals in a non-stationary, non-Gaussian background using a hidden Markov parameter estimator.
Radar systems are often used to detect signals from non-stationary and non-Gaussian environments. One type of system for detecting signals from such environments is described with reference to U.S. Pat. No. 5,694,342, entitled A Method for Detecting Signals In Non-Gaussian Background Clutter. In the system described in the '342 patent, input data represents a collection of successive intensities (norm squared) of baseband demodulated range-walk-corrected radar returns from a set of range bins organized into a range-by-pulse matrix X=(x.sub.ij), where R is the number of range bins, P is the number of pulses, and 1.ltoreq.i.ltoreq.R, 1.ltoreq.j.ltoreq.P. The data are filtered to partition range bins having exponentially distributed data from those that have non-exponentially distributed data. The intensities of the exponentially distributed data are estimated. Exponential mixture distributions are fit to each range bin of the non-exponential data. Then, noise parameters are selected for each range bin. The residual intensity of the data in each range bin is estimated. A detection statistic M.sub.i and the standard deviation N.sub.i are determined for each range bin. A normalized detection statistic S.sub.i is defined by S.sub.i =M.sub.i /N.sub.i. The maximum value, S.sub.max, and the mean, S.sub.mean, and standard deviation, S.sub.std, of all S.sub.i excluding S.sub.max are determined. A threshold .tau..sub..alpha. corresponding to a false alarm probability .alpha. is determined. An output signal is generated for range bin i if (S.sub.i -S.sub.max)/S.sub.std .gtoreq..tau..sub..alpha..
The system described in the '342 patent generally requires 50 or more data samples, and more preferably, 100+ data samples, to perform reasonable estimates of the model parameters. It would be desirable to develop a system that could detect weak signals in a non-Gaussian, non-stationary background which required fewer data samples.